def test__visibilities_and_model_are_identical__inversion_included__changes_certain_properties(
self):
visibilities_mask = np.full(fill_value=False, shape=(2, 2))
real_space_mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.visibilities.manual_1d(visibilities=[[1.0, 2.0], [3.0, 4.0]])
noise_map = aa.visibilities.manual_1d(
visibilities=[[2.0, 2.0], [2.0, 2.0]])
interferometer = aa.interferometer(visibilities=data,
noise_map=noise_map,
uv_wavelengths=np.ones(shape=(2,
2)))
masked_interferometer = aa.masked.interferometer(
interferometer=interferometer,
visibilities_mask=visibilities_mask,
real_space_mask=real_space_mask,
)
model_data = aa.visibilities.manual_1d(
visibilities=[[1.0, 2.0], [3.0, 4.0]])
inversion = mock_inversion.MockFitInversion(
regularization_term=2.0,
log_det_curvature_reg_matrix_term=3.0,
log_det_regularization_matrix_term=4.0,
)
fit = aa.fit(
masked_dataset=masked_interferometer,
model_data=model_data,
inversion=inversion,
)
assert fit.chi_squared == 0.0
assert fit.reduced_chi_squared == 0.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)
assert fit.likelihood_with_regularization == -0.5 * (
fit.chi_squared + 2.0 + fit.noise_normalization)
assert fit.evidence == -0.5 * (fit.chi_squared + 2.0 + 3.0 - 4.0 +
fit.noise_normalization)
assert fit.figure_of_merit == fit.evidence
def test__image_and_model_are_identical__inversion_included__changes_certain_properties(
self):
mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.masked.array.manual_1d(array=np.array([1.0, 2.0, 3.0, 4.0]),
mask=mask)
noise_map = aa.masked.array.manual_1d(array=np.array(
[2.0, 2.0, 2.0, 2.0]),
mask=mask)
imaging = aa.imaging(image=data, noise_map=noise_map)
masked_imaging = aa.masked.imaging(imaging=imaging, mask=mask)
model_data = aa.masked.array.manual_1d(array=np.array(
[1.0, 2.0, 3.0, 4.0]),
mask=mask)
inversion = mock_inversion.MockFitInversion(
regularization_term=2.0,
log_det_curvature_reg_matrix_term=3.0,
log_det_regularization_matrix_term=4.0,
)
fit = aa.fit(masked_dataset=masked_imaging,
model_data=model_data,
inversion=inversion)
assert fit.chi_squared == 0.0
assert fit.reduced_chi_squared == 0.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)
assert fit.likelihood_with_regularization == -0.5 * (
fit.chi_squared + 2.0 + fit.noise_normalization)
assert fit.evidence == -0.5 * (fit.chi_squared + 2.0 + 3.0 - 4.0 +
fit.noise_normalization)
assert fit.figure_of_merit == fit.evidence
def test__image_and_model_are_identical__no_masking__check_values_are_correct(
self):
mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.masked.array.manual_1d(array=np.array([1.0, 2.0, 3.0, 4.0]),
mask=mask)
noise_map = aa.masked.array.manual_1d(array=np.array(
[2.0, 2.0, 2.0, 2.0]),
mask=mask)
imaging = aa.imaging(image=data, noise_map=noise_map)
masked_imaging = aa.masked.imaging(imaging=imaging, mask=mask)
model_data = aa.masked.array.manual_1d(array=np.array(
[1.0, 2.0, 3.0, 4.0]),
mask=mask)
fit = aa.fit(masked_dataset=masked_imaging, model_data=model_data)
assert (fit.mask == np.array([[False, False], [False, False]])).all()
assert (fit.image.in_1d == np.array([1.0, 2.0, 3.0, 4.0])).all()
assert (fit.image.in_2d == np.array([[1.0, 2.0], [3.0, 4.0]])).all()
assert (fit.noise_map.in_1d == np.array([2.0, 2.0, 2.0, 2.0])).all()
assert (fit.noise_map.in_2d == np.array([[2.0, 2.0], [2.0,
2.0]])).all()
assert (fit.signal_to_noise_map.in_1d == np.array([0.5, 1.0, 1.5,
2.0])).all()
assert (fit.signal_to_noise_map.in_2d == np.array([[0.5, 1.0],
[1.5, 2.0]])).all()
assert (fit.model_image.in_1d == np.array([1.0, 2.0, 3.0, 4.0])).all()
assert (fit.model_image.in_2d == np.array([[1.0, 2.0], [3.0,
4.0]])).all()
assert (fit.residual_map.in_1d == np.array([0.0, 0.0, 0.0, 0.0])).all()
assert (fit.residual_map.in_2d == np.array([[0.0, 0.0], [0.0,
0.0]])).all()
assert (fit.normalized_residual_map.in_1d == np.array(
[0.0, 0.0, 0.0, 0.0])).all()
assert (fit.normalized_residual_map.in_2d == np.array([[0.0, 0.0],
[0.0,
0.0]])).all()
assert (fit.chi_squared_map.in_1d == np.array([0.0, 0.0, 0.0,
0.0])).all()
assert (fit.chi_squared_map.in_2d == np.array([[0.0, 0.0],
[0.0, 0.0]])).all()
assert fit.chi_squared == 0.0
assert fit.reduced_chi_squared == 0.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)
def test__visibilities_and_model_are_different__no_masking__check_values_are_correct(
self):
visibilities_mask = np.full(fill_value=False, shape=(2, 2))
real_space_mask = aa.mask.manual(
mask_2d=np.array([[False, False], [False, False]]),
sub_size=1,
pixel_scales=(1.0, 1.0),
)
data = aa.visibilities.manual_1d(visibilities=[[1.0, 2.0], [3.0, 4.0]])
noise_map = aa.visibilities.manual_1d(
visibilities=[[2.0, 2.0], [2.0, 2.0]])
interferometer = aa.interferometer(visibilities=data,
noise_map=noise_map,
uv_wavelengths=np.ones(shape=(2,
2)))
masked_interferometer = aa.masked.interferometer(
interferometer=interferometer,
visibilities_mask=visibilities_mask,
real_space_mask=real_space_mask,
)
model_data = aa.visibilities.manual_1d(
visibilities=[[1.0, 2.0], [3.0, 3.0]])
fit = aa.fit(masked_dataset=masked_interferometer,
model_data=model_data)
assert (fit.visibilities_mask == np.array([[False, False],
[False, False]])).all()
assert (fit.visibilities.in_1d == np.array([[1.0, 2.0], [3.0,
4.0]])).all()
assert (fit.noise_map.in_1d == np.array([[2.0, 2.0], [2.0,
2.0]])).all()
assert (fit.signal_to_noise_map.in_1d == np.array([[0.5, 1.0],
[1.5, 2.0]])).all()
assert (fit.model_visibilities.in_1d == np.array([[1.0, 2.0],
[3.0, 3.0]])).all()
assert (fit.residual_map.in_1d == np.array([[0.0, 0.0], [0.0,
1.0]])).all()
assert (fit.normalized_residual_map.in_1d == np.array([[0.0, 0.0],
[0.0,
0.5]])).all()
assert (fit.chi_squared_map.in_1d == np.array([[0.0, 0.0],
[0.0, 0.25]])).all()
assert fit.chi_squared == 0.25
assert fit.reduced_chi_squared == 0.25 / 4.0
assert fit.noise_normalization == np.sum(
np.log(2 * np.pi * noise_map**2.0))
assert fit.likelihood == -0.5 * (fit.chi_squared +
fit.noise_normalization)